1111

Using neural networks and GIS to forecast land use changes:

a Land Transformation Model應用 GIS及類神經元網路預測土地利用變

化之研究 :一種土地轉換模式

作者：作者： Bryan C. Pijanowski, Daniel G. Brown, Bradley A. Shellito,

Gaurav A. Manik

報告同學：詹傑閔報告同學：詹傑閔

22

33

Introduction

This paper illustrates how combining geographic information systems (GIS) and artificial neural networks (ANNs) can aid in the understanding

the complex process of land use change.

A GIS-based Land Transformation Model

(LTM)

to forecast land use change over large regions.

44

Background

ANNs(Artificial Neural Networks)

ANNs were developed to model the brain’s interconnected system of neurons so that computers could be made to imitate the brain’s ability to sort patterns and learn from trial and error, thus observing relationships in data.

55

Background

A. simple perceptronB. The multi-layer perceptron (MLP)

classifying linearly separable data and performing linear functions

The MLP consists of three layers: input, hidden, and output

Background GIS(Geographic Information System)

Geographic Information System is an advanced computer software technology. It is a variety of spatial information collection, storage, analysis and visualization of information processing and management system.

In the international study on land use change, mainly in support of GIS through remote sensing images of different periods or land-use diagram space Diejia operation, obtained the land use types during the transfer matrix, and then analyzes the status of land use change .

Background

77

88

Background

The LTM follows four sequential stepsThe LTM follows four sequential steps

Background

LTM has many factors,such as political, administrative, economic, cultural, human behavior and the environment, small roads, residential streets, rivers, lakes and so on.

LTM based on GIS technology is used to predict the large regional scale land use change. It uses a large number of socio-economic, political and environmental data and other information as the basis for the social, economic, political, ecological environment, land planners and resource managers to provide the necessary information.

Background

To recapitulate, LTM model in the following aspect is a powerful tool:

(1) When the social, economic and spatial variables driving the land use change occurs, the detection of a variety of mechanisms.(2) To predict the future potential for land use change.

(3) Assessment of the government management system and policies on land use and development patterns.

MethodsMethods

Study area and data sourcesStudy area and data sources Michigan’s Grand Traverse Bay Watershed (GTBW) Michigan’s Grand Traverse Bay Watershed (GTBW)

was selected as the test site for this project. The GTBW, was selected as the test site for this project. The GTBW, located in the northwestern portion of Michigan’s Lower located in the northwestern portion of Michigan’s Lower Peninsula, is Peninsula, is one of the most rapid population growth one of the most rapid population growth and land use change regions in the USAand land use change regions in the USA. .

From 1970 to 1997, resident population in the From 1970 to 1997, resident population in the watershed nearly watershed nearly doubleddoubled. Traverse City, with a resident . Traverse City, with a resident population of approximately 18,000 (oftentimes having a population of approximately 18,000 (oftentimes having a seasonal tourist population exceeding 500,000) is seasonal tourist population exceeding 500,000) is the the largest city in the watershedlargest city in the watershed..

1111

MethodsMethods Map of Michigan’s Grand Traverse Bay Watershed counties and Map of Michigan’s Grand Traverse Bay Watershed counties and

important locations within the watershed.important locations within the watershed.

1212

MethodsMethods

1313

MethodsMethods

GIS-based predictor variablesGIS-based predictor variables

1414

MethodsMethods Maps of the 10-predictor variables used for the training exercise.Maps of the 10-predictor variables used for the training exercise.

Ten predictor variables and the exclusion zones were compiled in Arc/Info Grid format using the LTM GIS Avenue interface.

format (Table 1; Fig. 3) using the LTM GIS Avenue interface.format (Table 1; Fig. 3) using the LTM GIS Avenue interface.

1515

MethodsMethods Maps of the 10-predictor variables used for the training exercise.Maps of the 10-predictor variables used for the training exercise.

1616

MethodsMethods Maps of the 10-predictor variables used for the training exercise.Maps of the 10-predictor variables used for the training exercise.

1717

MethodsMethods Maps of the 10-predictor variables used for the training exercise.Maps of the 10-predictor variables used for the training exercise.

1818

MethodsMethods Maps of the 10-predictor variables used for the training exercise.Maps of the 10-predictor variables used for the training exercise.

1919

MethodsMethods

ANN-based integrationANN-based integration ANNs were applied to the prediction of land use change ANNs were applied to the prediction of land use change

in four phases:in four phases:

2020

(1) Use of GIS spatial data layer as the input layer ANNs(2) The use of small areas of historical data as the sample data (3) The extended network, all of the study area using historical data (4) The use of information obtained predictions

MethodsMethods

LTM application of the model is broadly as follows:

The re-classification of land types with the code, and information and data of the drawings after the available information regarding the transport network, rivers, lakes, coastline location, etc. As a combination of GIS-based LTM model input data.

MethodsMethods

In the establishment of GIS-based land use change model, based on past historical data of a large number of mass analysis, we can see land use change and population trends; then the impact of land use in the population, political, economic, transportation and other elements of graph Overlay analysis to determine the elements of the new integrated impact of land use. The conclusions of the data will be generated as the ANNs spatial data layer input data to predict.

MethodsMethods An overlay of model predictions and observed changes in an area An overlay of model predictions and observed changes in an area

southwest of Traverse City in Grand Traverse County.southwest of Traverse City in Grand Traverse County.

2323

Results and discussionResults and discussion

Watershed-scale land use projectionsWatershed-scale land use projections

TheseThese projections illustrate how the ANN could be projections illustrate how the ANN could be trained on relationships betweentrained on relationships between urbanization and all of urbanization and all of the predictor variables that occurred in Grand Traversethe predictor variables that occurred in Grand Traverse

County and, through our approach, applied to the same County and, through our approach, applied to the same predictor variables scaledpredictor variables scaled to a larger region to provide to a larger region to provide reasonable results for these counties in the watershed.reasonable results for these counties in the watershed.

2424

Results and discussionResults and discussion ThisThis Fig. shows the results of this regional forecast of land use Fig. shows the results of this regional forecast of land use

changeschanges

2525

Results and discussionResults and discussion

Land use change prediction is the use of many different periods to obtain source of information on their comprehensive analysis and comparison, based on the changes that change the type of region and it is a data-based learning and analysis process, which is in line with ANN technology features.

ANN analysis of information processing capabilities of the GIS to make up for the lack of dynamic data analysis. It can be developed based on historical data to a certain variation of induction, and then to predict.

ConclusionsConclusions

We made several We made several assumptionsassumptions in order to keep the model in order to keep the model simple:simple:

(1) the pattern of each predictor variable remained constant beyond (1) the pattern of each predictor variable remained constant beyond 1990.1990.

(2) spatial rules used to build the interactions between the predictor cells (2) spatial rules used to build the interactions between the predictor cells and potential locations for transition are assumed to be correct and remain and potential locations for transition are assumed to be correct and remain constant over time.constant over time.

(3) the neural network itself was assumed to remain constant over time.(3) the neural network itself was assumed to remain constant over time.

(4) the amount of urban per capita(4) the amount of urban per capita

undergoing a transition is assumed to be fixed over time.undergoing a transition is assumed to be fixed over time.

2727

ConclusionsConclusions

Changes using GIS and ANN to ANN forecasting Changes using GIS and ANN to ANN forecasting method takes advantage of the high degree of method takes advantage of the high degree of complexity of mapping abilitycomplexity of mapping ability and Strong self-organizing and Strong self-organizing and adaptive learning capacity ability to multi-source and adaptive learning capacity ability to multi-source data fusion , detection accuracy and efficiency in a data fusion , detection accuracy and efficiency in a greater increase.greater increase.

During the network training, the use of existing GIS data-During the network training, the use of existing GIS data-aided training samples Selected to achieve the aided training samples Selected to achieve the automation of sample points on the part of the selection automation of sample points on the part of the selection of training samples can improve the efficiency of of training samples can improve the efficiency of selection.selection.

2828

ConclusionsConclusions

When using ANN predicted by the size and timing across When using ANN predicted by the size and timing across the region-wide restrictions on the length, because some the region-wide restrictions on the length, because some of the major impact Difficult to determine the of the major impact Difficult to determine the characteristics of elements, so the prediction is not characteristics of elements, so the prediction is not entirely accurate. entirely accurate.

The need for the use of GIS data generated by way of a The need for the use of GIS data generated by way of a deeper level, We believe that with the continuous deeper level, We believe that with the continuous progress of science and technology will be more progress of science and technology will be more accurate forecasting results.accurate forecasting results.

2929